In this section we study a class of

In this section we study a class of item-based recommendation
algorithms for producing predictions to users. Unlike
the user-based collaborative ltering algorithm discussed in
Section 2, the item-based approach looks into the set of
items the target user has rated and computes how similar
they are to the target item i and then selects k most
similar items fi1; i2;::: ;ikg. At the same time their cor- responding similarities fsi1; si2;::: ;sikg are also computed.
Once the most similar items are found, the prediction is
then computed by taking a weighted average of the target
user's ratings on these similar items. We describe these two
aspects, namely, the similarity computation and the prediction
generation in details here.

ผลลัพธ์ (อังกฤษ) 2:

In this Section we Study of a class-based Item Recommendation
algorithms for producing Predictions to users. Unlike
The user-based collaborative filtering algorithm discussed in
Section 2, The Item-based Approach Looks Into The Set of
Items The Target user has rated and computes How similar
are they to The Target Item I and then SELECTS K Most
Items similar Fi1; i2; :::; ikg. At the same time their cor- responding similarities fsi1; si2; :::; Sikg are also computed.
Once The Most are similar Items Found, The Prediction is
then computed by a weighted Taking The Target Average of
user's ratings on these Items similar. Two we describe these
aspects, namely, The similarity Computation and The Prediction
Generation in details here.